Goyal Mehendi, Khanna Divya, Rana Prashant Singh, Khaibullin Timur, Martynova Ekaterina, Rizvanov Albert A, Khaiboullina Svetlana F, Baranwal Manoj
Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India.
Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India.
Front Neurol. 2019 Jul 18;10:781. doi: 10.3389/fneur.2019.00781. eCollection 2019.
Multiple sclerosis (MS) is a neurodegenerative disease characterized by lesions in the central nervous system (CNS). Inflammation and demyelination are the leading causes of neuronal death and brain lesions formation. The immune reactivity is believed to be essential in the neuronal damage in MS. Cytokines play important role in differentiation of Th cells and recruitment of auto-reactive B and T lymphocytes that leads to neuron demyelination and death. Several cytokines have been found to be linked with MS pathogenesis. In the present study, serum level of eight cytokines (IL-1β, IL-2, IL-4, IL-8, IL-10, IL-13, IFN-γ, and TNF-α) was analyzed in USA and Russian MS to identify predictors for the disease. Further, the model was extended to classify MS into remitting and non-remitting by including age, gender, disease duration, Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Severity Score (MSSS) into the cytokines datasets in Russian cohorts. The individual serum cytokines data for the USA cohort was generated by Z score percentile method using R studio, while serum cytokines of the Russian cohort were analyzed using multiplex immunoassay. Datasets were divided into training (70%) and testing (30%). These datasets were used as an input into four machine learning models (support vector machine, decision tree, random forest, and neural networks) available in R programming language. Random forest model was identified as the best model for diagnosis of MS as it performed remarkable on all the considered criteria i.e., Gini, accuracy, specificity, AUC, and sensitivity. RF model also performed best in predicting remitting and non-remitting MS. The present study suggests that the concentration of serum cytokines could be used as prognostic markers for the prediction of MS.
多发性硬化症（MS）是一种以中枢神经系统（CNS）病变为特征的神经退行性疾病。炎症和脱髓鞘是神经元死亡和脑损伤形成的主要原因。免疫反应被认为在MS的神经元损伤中至关重要。细胞因子在Th细胞分化以及自身反应性B和T淋巴细胞的募集过程中发挥重要作用，进而导致神经元脱髓鞘和死亡。已发现多种细胞因子与MS发病机制有关。在本研究中，分析了美国和俄罗斯MS患者血清中八种细胞因子（IL-1β、IL-2、IL-4、IL-8、IL-10、IL-13、IFN-γ和TNF-α）的水平，以确定该疾病的预测指标。此外，通过将年龄、性别、病程、扩展残疾状态量表（EDSS）和多发性硬化症严重程度评分（MSSS）纳入俄罗斯队列的细胞因子数据集中，将模型扩展以将MS分为缓解型和非缓解型。美国队列的个体血清细胞因子数据通过使用R studio的Z评分百分位数法生成，而俄罗斯队列的血清细胞因子则使用多重免疫测定法进行分析。数据集分为训练集（70%）和测试集（30%）。这些数据集被用作R编程语言中可用的四种机器学习模型（支持向量机、决策树、随机森林和神经网络）的输入。随机森林模型被确定为诊断MS的最佳模型，因为它在所有考虑的标准（即基尼系数、准确性、特异性、AUC和敏感性）上表现出色。RF模型在预测缓解型和非缓解型MS方面也表现最佳。本研究表明，血清细胞因子浓度可作为预测MS的预后标志物。
